Diagnosis assistance system and control method thereof

Abstract

The present invention relates to a diagnosis assistance system for assisting diagnosis for a plurality of diseases based on a fundus image, the diagnosis assistance system including: a fundus image obtaining unit configured to obtain a fundus image; a first processing unit configured to, for the fundus image, obtain a first result related to a first finding of a patient using a first neural network model, a second processing unit configured to, for the fundus image, obtain a second result related to a second finding of the patient using a second neural network model, a third processing unit configured to determine, on the basis of the first result and the second result, diagnostic information on the patient, and a diagnostic information output unit configured to provide the determined diagnostic information to a user.

Description

CROSS-REFERENCE TO RELATED APPLICATION[0001]This application is a continuation of PCT / KR2018 / 009809 filed on Aug. 24, 2018, which claims priority to Republic of Korea Patent Application No. 10-2017-0108232 filed on Aug. 25, 2017, which are incorporated by reference herein in their entirety.TECHNICAL FIELD[0002]The present invention relates to a diagnosis assistance system and control method thereof, and more particularly, to a diagnosis assistance system and control method thereof for providing diagnosis assistance information on the basis of an image.BACKGROUND ART[0003]The fundus examination is a diagnosis assistance material frequently utilized in ophthalmology since it is able to observe the abnormalities of the retina, optic nerve, and macula and allows the results to be confirmed by relatively simple imaging. In recent years, the fundus examination has been increasingly used because, through the fundus examination, it is able to observe not only eye diseases but also a degree ...

AI Technical Summary

Benefits of technology

The present invention provides a method for quickly getting multiple pieces of information from a fundus image using a machine learned model. This method involves pre-processing the fundus image, training the model to identify specific risks or indicators, and then using the trained model to analyze new fundus images. This approach can help healthcare professionals more efficiently and accurately diagnose and treat diseases related to the retina.

Problems solved by technology

However, in a case in which a plurality of values are desired to be predicted from a single test data through a deep learning trained model, there has been a problem in that accuracy of prediction is reduced and processing speed is lowered.

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